scholarly journals XCO<sub>2</sub> retrieval for GOSAT and GOSAT-2 based on the FOCAL algorithm

2021 ◽  
Vol 14 (5) ◽  
pp. 3837-3869
Author(s):  
Stefan Noël ◽  
Maximilian Reuter ◽  
Michael Buchwitz ◽  
Jakob Borchardt ◽  
Michael Hilker ◽  
...  
Keyword(s):  
Water Vapour ◽  
Bias Correction ◽  
Near Infrared ◽  
Temporal Variations ◽  
Total Carbon ◽  
Time Interval ◽  
Data Sets ◽  
Cloud Detection ◽  
First Results ◽  
Shortwave Infrared

Abstract. Since 2009, the Greenhouse gases Observing SATellite (GOSAT) has performed radiance measurements in the near-infrared (NIR) and shortwave infrared (SWIR) spectral region. From February 2019 onward, data from GOSAT-2 have also been available. We present the first results from the application of the Fast atmOspheric traCe gAs retrievaL (FOCAL) algorithm to derive column-averaged dry-air mole fractions of carbon dioxide (XCO2) from GOSAT and GOSAT-2 radiances and their validation. FOCAL was initially developed for OCO-2 XCO2 retrievals and allows simultaneous retrievals of several gases over both land and ocean. Because FOCAL is accurate and numerically very fast, it is currently being considered as a candidate algorithm for the forthcoming European anthropogenic CO2 Monitoring (CO2M) mission to be launched in 2025. We present the adaptation of FOCAL to GOSAT and discuss the changes made and GOSAT specific additions. This particularly includes modifications in pre-processing (e.g. cloud detection) and post-processing (bias correction and filtering). A feature of the new application of FOCAL to GOSAT and GOSAT-2 is the independent use of both S- and P-polarisation spectra in the retrieval. This is not possible for OCO-2, which measures only one polarisation direction. Additionally, we make use of GOSAT's wider spectral coverage compared to OCO-2 and derive not only XCO2, water vapour (H2O), and solar-induced fluorescence (SIF) but also methane (XCH4), with the potential for further atmospheric constituents and parameters like semi-heavy water vapour (HDO). In the case of GOSAT-2, the retrieval of nitrous oxide (XN2O) and carbon monoxide (CO) may also be possible. Here, we concentrate on the new FOCAL XCO2 data products. We describe the generation of the products as well as applied filtering and bias correction procedures. GOSAT-FOCAL XCO2 data have been produced for the time interval 2009 to 2019. Comparisons with other independent GOSAT data sets reveal agreement of long-term temporal variations within about 1 ppm over 1 decade; differences in seasonal variations of about 0.5 ppm are observed. Furthermore, we obtain a station-to-station bias of the new GOSAT-FOCAL product to the ground-based Total Carbon Column Observing Network (TCCON) of 0.56 ppm with a mean scatter of 1.89 ppm. The GOSAT-2-FOCAL XCO2 product is generated in a similar way as the GOSAT-FOCAL product, but with adapted settings. All GOSAT-2 data until the end of 2019 have been processed. Because of this limited time interval, the GOSAT-2 results are considered to be preliminary only, but first comparisons show that these data compare well with the GOSAT-FOCAL results and also TCCON.

scholarly journals XCO<sub>2</sub> retrieval for GOSAT and GOSAT-2 based on the FOCAL algorithm

2020 ◽  
Author(s):  
Stefan Noël ◽  
Maximilian Reuter ◽  
Michael Buchwitz ◽  
Jakob Borchardt ◽  
Michael Hilker ◽  
...  
Keyword(s):  
Water Vapour ◽  
Bias Correction ◽  
Temporal Variations ◽  
Total Carbon ◽  
Time Interval ◽  
Data Sets ◽  
Cloud Detection ◽  
Co2 Monitoring ◽  
First Results ◽  
Shortwave Infrared

Abstract. Since 2009, the Greenhouse gases Observing SATellite (GOSAT) performs radiance measurements in the shortwave-infrared (SWIR) spectral region. From February 2019 onward, data from GOSAT-2 are also available. We present first results from the application of the Fast atmOspheric traCe gAs retrieval (FOCAL) algorithm to derive column-averaged dry-air mole fractions of carbon dioxide (XCO2) from GOSAT and GOSAT-2 radiances and their validation. FOCAL has initially been developed for OCO-2 XCO2 retrievals and allows simultaneous retrievals of several gases over both land and ocean. Because FOCAL is accurate and numerically very fast it is currently considered as a candidate algorithm for the forthcoming European anthropogenic CO2 Monitoring (CO2M) mission, to be launched in 2025. We present the adaptation of FOCAL to GOSAT and discuss the changes made and GOSAT specific additions. This includes particularly modifications in pre-processing (e.g. cloud detection) and post-processing (bias correction and filtering). A feature of the new application of FOCAL to GOSAT/GOSAT-2 is the independent use of both S and P polarisation spectra in the retrieval. This is not possible for OCO-2, which measures only one polarisation direction. Additionally, we make use of GOSAT’s wider spectral coverage compared to OCO-2 and derive not only XCO2, water vapour (H2O) and solar induced fluorescence (SIF) but also methane (XCH4), with the potential for further atmospheric constituents and parameters like semiheavy water vapour (HDO) and (in the case of GOSAT-2) also carbon monoxide (CO) total columns and possibly nitrous oxide (XN2O). Here, we concentrate on the new FOCAL XCO2 data products. We describe the generation of the products as well as applied filtering and bias correction procedures. GOSAT-FOCAL XCO2 data have been produced for the time interval 2009 to 2019. Comparisons with other independent GOSAT data sets reveal an agreement of long-term temporal variations within about 1 ppm over one decade; differences in seasonal variations of about 0.5 ppm are observed. Furthermore, we obtain a mean regional bias of the new GOSAT-FOCAL product to the ground based Total Carbon Column Observing Network (TCCON) of 0.56 ppm with a mean scatter of 1.89 ppm. The GOSAT-2-FOCAL XCO2 product is generated in a similar way as the GOSAT-FOCAL product, but with adapted settings. All GOSAT-2 data until end of 2019 have been processed. Because of this limited time interval, the GOSAT-2 results are considered to be preliminary only, but first comparisons show that these data compare well with the GOSAT-FOCAL results.

Carbon dioxide and methane RemoTeC retrievals from GOSAT (2009-2019) and GOSAT-2 (2019)

2020 ◽  
Author(s):  
Markus Haun ◽  
Hiroshi Suto ◽  
André Butz
Keyword(s):  
Carbon Dioxide ◽  
Bias Correction ◽  
Total Carbon ◽  
Dry Air ◽  
The Earth ◽  
First Results ◽  
Process Measurements ◽  
Spectral Ranges ◽  
Atmospheric Concentrations ◽  
Band Signal

&lt;p&gt;The Japanese Greenhouse gases Observing SATellite (GOSAT), in orbit since January 2009, and its successor GOSAT-2, in orbit since October 2018, are dedicated to enhancing our knowledge of the carbon cycle. We use our RemoTeC full-physics algorithm to infer atmospheric concentrations and auxiliary parameters from the measured solar SWIR radiances backscattered at the earth&amp;#180;s atmosphere and surface. Accuracy requirements are a major challenge for these space-based measurements of column-averaged dry-air mole fractions of carbon dioxide and methane (XCO&lt;sub&gt;2&lt;/sub&gt; and XCH&lt;sub&gt;4&lt;/sub&gt;).&lt;/p&gt;&lt;p&gt;Here we present 1) a consolidated ten years GOSAT XCO&lt;sub&gt;2&lt;/sub&gt; and XCH&lt;sub&gt;4&lt;/sub&gt; dataset and 2) first results for GOSAT-2 XCO&lt;sub&gt;2&lt;/sub&gt; and XCH&lt;sub&gt;4&lt;/sub&gt; retrievals. For GOSAT, 4.8 % of all measurements pass our cloud filter, based on the O&lt;sub&gt;2&lt;/sub&gt; A-band signal and several quality filters. For validation and bias-correction, we use collocated measurements from ground-based stations of the Total Carbon Column Observing Network (TCCON). The dataset shows a scatter of well below 1 %. For GOSAT-2, we are able to reliably process measurements in land nadir and ocean glint geometry. In addition to the retrieval windows implemented for GOSAT, we use the spectral ranges at 4290 - 4328 cm&lt;sup&gt;-1&lt;/sup&gt; and 4354 - 4441 cm&lt;sup&gt;-1&lt;/sup&gt; to retrieve carbon monoxide and nitrous oxide concentrations. We evaluate instrument performance and precision by comparing XCO&lt;sub&gt;2&lt;/sub&gt; and XCH&lt;sub&gt;4&lt;/sub&gt; retrievals to both collocated GOSAT and ground-based TCCON measurements.&lt;/p&gt;

scholarly journals Study of the footprints of short-term variation in XCO<sub>2</sub> observed by TCCON sites using NIES and FLEXPART atmospheric transport models

2016 ◽  
Author(s):  
D. A. Belikov ◽  
S. Maksyutov ◽  
A. Ganshin ◽  
R. Zhuravlev ◽  
N. M. Deutscher ◽  
...  
Keyword(s):  
Near Infrared ◽  
Transport Model ◽  
Atmospheric Transport ◽  
Dispersion Model ◽  
Three Dimensional ◽  
Temporal Variations ◽  
Particle Dispersion ◽  
Bias Reduction ◽  
Total Carbon ◽  
Short Term

Abstract. The Total Carbon Column Observing Network (TCCON) is a network of ground-based Fourier Transform Spectrometers (FTS) that record near-infrared (NIR) spectra of the Sun. From these spectra, accurate and precise observations of CO2 column-averaged dry-air mole fraction (denoted XCO2) are retrieved. TCCON FTS observations have previously been used to validate satellite estimations of XCO2; however, our knowledge of the short-term spatial and temporal variations in XCO2 surrounding the TCCON sites is limited. In this work, we use the National Institute for Environmental Studies (NIES) Eulerian three-dimensional transport model and the FLEXPART (FLEXible PARTicle) Lagrangian Particle Dispersion Model (LPDM) to determine the footprints of short-term variations in XCO2 observed by operational, past, future, and possible TCCON sites. We propose a footprint-based method for the colocation of satellite and TCCON XCO2 observations, and estimate the performance of the method using the NIES model and five GOSAT XCO2 product datasets. Comparison of the proposed approach with a standard geographic method shows higher number of colocation points and average bias reduction up to 0.15 ppm for a subset of 16 stations for the period from January 2010 to January 2014. Case studies of the Darwin and La Réunion sites reveal that when the footprint area is rather curved, non-uniform and significantly different from a geographical rectangular area, the differences between these approaches are more noticeable. This emphasizes that the colocation is sensitive to local meteorological conditions and flux distributions.

scholarly journals Study of the footprints of short-term variation in XCO<sub>2</sub> observed by TCCON sites using NIES and FLEXPART atmospheric transport models

2017 ◽  
Vol 17 (1) ◽  
pp. 143-157 ◽  
Author(s):  
Dmitry A. Belikov ◽  
Shamil Maksyutov ◽  
Alexander Ganshin ◽  
Ruslan Zhuravlev ◽  
Nicholas M. Deutscher ◽  
...  
Keyword(s):  
Near Infrared ◽  
Transport Model ◽  
Atmospheric Transport ◽  
Dispersion Model ◽  
Three Dimensional ◽  
Temporal Variations ◽  
Particle Dispersion ◽  
Bias Reduction ◽  
Total Carbon ◽  
Short Term

Abstract. The Total Carbon Column Observing Network (TCCON) is a network of ground-based Fourier transform spectrometers (FTSs) that record near-infrared (NIR) spectra of the sun. From these spectra, accurate and precise observations of CO2 column-averaged dry-air mole fractions (denoted XCO2) are retrieved. TCCON FTS observations have previously been used to validate satellite estimations of XCO2; however, our knowledge of the short-term spatial and temporal variations in XCO2 surrounding the TCCON sites is limited. In this work, we use the National Institute for Environmental Studies (NIES) Eulerian three-dimensional transport model and the FLEXPART (FLEXible PARTicle dispersion model) Lagrangian particle dispersion model (LPDM) to determine the footprints of short-term variations in XCO2 observed by operational, past, future and possible TCCON sites. We propose a footprint-based method for the collocation of satellite and TCCON XCO2 observations and estimate the performance of the method using the NIES model and five GOSAT (Greenhouse Gases Observing Satellite) XCO2 product data sets. Comparison of the proposed approach with a standard geographic method shows a higher number of collocation points and an average bias reduction up to 0.15 ppm for a subset of 16 stations for the period from January 2010 to January 2014. Case studies of the Darwin and Reunion Island sites reveal that when the footprint area is rather curved, non-uniform and significantly different from a geographical rectangular area, the differences between these approaches are more noticeable. This emphasises that the collocation is sensitive to local meteorological conditions and flux distributions.

scholarly journals Remote sensing of water vapour profiles in the framework of the Total Carbon Column Observing Network (TCCON)

2010 ◽  
Vol 3 (4) ◽  
pp. 3987-4007
Author(s):  
M. Schneider ◽  
E. Sepúlveda ◽  
O. García ◽  
F. Hase ◽  
T. Blumenstock
Keyword(s):  
Water Vapour ◽  
Near Infrared ◽  
Total Carbon ◽  
Solar Absorption ◽  
Upper Troposphere ◽  
High Measurement ◽  
The Vertical Distribution

Abstract. We show that the near infrared solar absorption spectra recorded in the framework of the Total Carbon Column Observing Network (TCCON) can be used to derive the vertical distribution of tropospheric water vapour. Using spectral H2O signatures in the 4500–4700 cm−1 region one can well distinguish lower from middle/upper tropospheric water vapour concentrations. The vertical resolution is about 3 and 6 km, for the lower and middle/upper troposphere, respectively. We document the quality of the remotely-sensed profiles by comparisons with coincident in-situ Vaisala RS92 radiosonde measurements. The agreement of both techniques is very satisfactory. Due to the long-term strategy of the network and the high measurement frequency, the TCCON water vapour profile data offer novel opportunities for estimating the water vapour variability at different time scales and altitudes.

scholarly journals HYPERSPECTRAL ANOMALY DETECTION IN URBAN SCENARIOS

2016 ◽  
Vol XLI-B7 ◽  
pp. 111-116 ◽  
Author(s):  
J. G. Rejas Ayuga ◽  
R. Martínez Marín ◽  
M. Marchamalo Sacristán ◽  
J. Bonatti ◽  
J. C. Ojeda
Keyword(s):  
Anomaly Detection ◽  
Near Infrared ◽  
Hyperspectral Data ◽  
Detection Methods ◽  
San Jose ◽  
First Results ◽  
Anomaly Detector ◽  
A New Technique ◽  
Spectral Ranges ◽  
Shortwave Infrared

We have studied the spectral features of reflectance and emissivity in the pattern recognition of urban materials in several single hyperspectral scenes through a comparative analysis of anomaly detection methods and their relationship with city surfaces with the aim to improve information extraction processes. Spectral ranges of the visible-near infrared (VNIR), shortwave infrared (SWIR) and thermal infrared (TIR) from hyperspectral data cubes of AHS sensor and HyMAP and MASTER of two cities, Alcalá de Henares (Spain) and San José (Costa Rica) respectively, have been used. &lt;br&gt;&lt;br&gt; In this research it is assumed no prior knowledge of the targets, thus, the pixels are automatically separated according to their spectral information, significantly differentiated with respect to a background, either globally for the full scene, or locally by image segmentation. Several experiments on urban scenarios and semi-urban have been designed, analyzing the behaviour of the standard RX anomaly detector and different methods based on subspace, image projection and segmentation-based anomaly detection methods. A new technique for anomaly detection in hyperspectral data called DATB (Detector of Anomalies from Thermal Background) based on dimensionality reduction by projecting targets with unknown spectral signatures to a background calculated from thermal spectrum wavelengths is presented. First results and their consequences in non-supervised classification and extraction information processes are discussed.

scholarly journals Remote sensing of water vapour profiles in the framework of the Total Carbon Column Observing Network (TCCON)

2010 ◽  
Vol 3 (6) ◽  
pp. 1785-1795 ◽  
Author(s):  
M. Schneider ◽  
E. Sepúlveda ◽  
O. García ◽  
F. Hase ◽  
T. Blumenstock
Keyword(s):  
Water Vapour ◽  
Near Infrared ◽  
Total Carbon ◽  
Vertical Resolution ◽  
Profile Data ◽  
Solar Absorption ◽  
High Measurement ◽  
The Vertical Distribution

Abstract. We show that the near infrared solar absorption spectra recorded in the framework of the Total Carbon Column Observing Network (TCCON) can be used to derive the vertical distribution of tropospheric water vapour. The resolution of the TCCON spectra of 0.02 cm−1 is sufficient for retrieving lower and middle/upper tropospheric water vapour concentrations with a vertical resolution of about 3 and 8 km, respectively. We document the good quality of the remotely-sensed profiles by comparisons with coincident in-situ Vaisala RS92 radiosonde measurements. Due to the high measurement frequency, the TCCON water vapour profile data offer novel opportunities for estimating the water vapour variability at different timescales and altitudes.

scholarly journals Evaluation of MOPITT Version 7 joint TIR–NIR X<sub>CO</sub> retrievals with TCCON

2019 ◽  
Vol 12 (10) ◽  
pp. 5547-5572 ◽  
Author(s):  
Jacob K. Hedelius ◽  
Tai-Long He ◽  
Dylan B. A. Jones ◽  
Bianca C. Baier ◽  
Rebecca R. Buchholz ◽  
...  
Keyword(s):  
Bias Correction ◽  
Near Infrared ◽  
Mean Squared Error ◽  
Signal To Noise Ratio ◽  
Small Region ◽  
Global Scale ◽  
Total Carbon ◽  
Along Track ◽  
Global Bias ◽  
Bias Corrections

Abstract. Observations of carbon monoxide (CO) from the Measurements Of Pollution In The Troposphere (MOPITT) instrument aboard the Terra spacecraft were expected to have an accuracy of 10 % prior to the launch in 1999. Here we evaluate MOPITT Version 7 joint (V7J) thermal-infrared and near-infrared (TIR–NIR) retrieval accuracy and precision and suggest ways to further improve the accuracy of the observations. We take five steps involving filtering or bias corrections to reduce scatter and bias in the data relative to other MOPITT soundings and ground-based measurements. (1) We apply a preliminary filtering scheme in which measurements over snow and ice are removed. (2) We find a systematic pairwise bias among the four MOPITT along-track detectors (pixels) on the order of 3–4 ppb with a small temporal trend, which we remove on a global scale using a temporally trended bias correction. (3) Using a small-region approximation (SRA), a new filtering scheme is developed and applied based on additional quality indicators such as the signal-to-noise ratio (SNR). After applying these new filters, the root-mean-squared error computed using the local median from the SRA over 16 years of global observations decreases from 3.84 to 2.55 ppb. (4) We also use the SRA to find variability in MOPITT retrieval anomalies that relates to retrieval parameters. We apply a bias correction to one parameter from this analysis. (5) After applying the previous bias corrections and filtering, we compare the MOPITT results with the GGG2014 ground-based Total Carbon Column Observing Network (TCCON) observations to obtain an overall global bias correction. These comparisons show that MOPITT V7J is biased high by about 6 %–8 %, which is similar to past studies using independent validation datasets on V6J. When using TCCON spectrometric column retrievals without the standard airmass correction or scaling to aircraft (WMO scale), the ground- and satellite-based observations overall agree to better than 0.5 %. GEOS-Chem data assimilations are used to estimate the influence of filtering and scaling to TCCON on global CO and tend to pull concentrations away from the prior fluxes and closer to the truth. We conclude with suggestions for further improving the MOPITT data products.

scholarly journals The SPARC water vapour assessment II: Comparison of annual, semi-annual and quasi-biennial variations in stratospheric and lower mesospheric water vapour observed from satellites

2016 ◽  
Author(s):  
Stefan Lossow ◽  
Farahnaz Khosrawi ◽  
Gerald E. Nedoluha ◽  
Faiza Azam ◽  
Klaus Bramstedt ◽  
...  
Keyword(s):  
Water Vapour ◽  
Annual Variation ◽  
Temporal Variations ◽  
Data Sets ◽  
Comprehensive Overview ◽  
Modelling Studies ◽  
Non Local ◽  
The Individual ◽  
Good Agreement ◽  
Phase Differences

Abstract. In the framework of the second SPARC (Stratosphere-troposphere Processes And their Role in Climate) water assessment (WAVAS-II), the amplitudes and phases of the annual, semi-annual and quasi-biennial variation in stratospheric and lower mesospheric water were compared considering 32 data sets from 13 different satellite instruments. These comparisons aimed to provide a comprehensive overview of the typical uncertainties in the observational database which can be considered in subsequent observational and modelling studies. For the amplitudes, a good agreement of their latitude and altitude distribution was found. Quantitatively there were differences in particular at high latitudes, close to the tropopause and in the lower mesosphere. Here the standard deviation over all data sets typically exceeded 0.2 ppmv for the annual variation and 0.1 ppmv for the semi-annual and quasi-biennial variation. For the phase, larger differences between the data sets were found in the lower mesosphere. Generally the smallest phase uncertainties can be observed in regions where the amplitude of the variability is large. The standard deviations over all data sets were typically smaller than a month for the annual and semi-annual variation and smaller than 5 months for the quasi-biennial variation. The amplitude and phase differences among the data sets could be explained by a combination of reasons. An important role play temporal variations of systematic errors and differences in the temporal and spatial sampling. In addition, differences in the considered time periods, the vertical resolution of the data, influences of clouds, aerosols as well as non local thermodynamic equilibrium (NLTE) effects cause differences between the individual data sets.

scholarly journals HYPERSPECTRAL ANOMALY DETECTION IN URBAN SCENARIOS

2016 ◽  
Vol XLI-B7 ◽  
pp. 111-116
Author(s):  
J. G. Rejas Ayuga ◽  
R. Martínez Marín ◽  
M. Marchamalo Sacristán ◽  
J. Bonatti ◽  
J. C. Ojeda
Keyword(s):  
Anomaly Detection ◽  
Near Infrared ◽  
Hyperspectral Data ◽  
Detection Methods ◽  
San Jose ◽  
First Results ◽  
Anomaly Detector ◽  
A New Technique ◽  
Spectral Ranges ◽  
Shortwave Infrared

We have studied the spectral features of reflectance and emissivity in the pattern recognition of urban materials in several single hyperspectral scenes through a comparative analysis of anomaly detection methods and their relationship with city surfaces with the aim to improve information extraction processes. Spectral ranges of the visible-near infrared (VNIR), shortwave infrared (SWIR) and thermal infrared (TIR) from hyperspectral data cubes of AHS sensor and HyMAP and MASTER of two cities, Alcalá de Henares (Spain) and San José (Costa Rica) respectively, have been used. <br><br> In this research it is assumed no prior knowledge of the targets, thus, the pixels are automatically separated according to their spectral information, significantly differentiated with respect to a background, either globally for the full scene, or locally by image segmentation. Several experiments on urban scenarios and semi-urban have been designed, analyzing the behaviour of the standard RX anomaly detector and different methods based on subspace, image projection and segmentation-based anomaly detection methods. A new technique for anomaly detection in hyperspectral data called DATB (Detector of Anomalies from Thermal Background) based on dimensionality reduction by projecting targets with unknown spectral signatures to a background calculated from thermal spectrum wavelengths is presented. First results and their consequences in non-supervised classification and extraction information processes are discussed.

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